code: def _step_func(self, func, t0, dt, t1, y0):
      f0 = func(
          t0, y0, perturb=Perturb.NEXT if self.perturb else Perturb.NONE
      )
      self._update_history(t0, f0)
      order = min(len(self.prev_f), self.max_order - 1)
      if order < _MIN_ORDER - 1:
          # Compute using RK4.
          return (
              rk4_alt_step_func(
                  func,
                  t0,
                  dt,
                  t1,
                  y0,
                  f0=self.prev_f[0],
                  perturb=self.perturb,
              ),
              f0,
          )
      else:
          # Adams-Bashforth predictor.
          bashforth_coeffs = self.bashforth[order]
          dy = _dot_product(dt * bashforth_coeffs, self.prev_f).type_as(
              y0
          )  # bashforth is float64 so cast back
  
          # Adams-Moulton corrector.
          if self.implicit:
              moulton_coeffs = self.moulton[order + 1]
              delta = dt * _dot_product(
                  moulton_coeffs[1:], self.prev_f
              ).type_as(
                  y0
              )  # moulton is float64 so cast back
              converged = False
              for _ in range(self.max_iters):
                  dy_old = dy
                  f = func(
                      t1,
                      y0 + dy,
                      perturb=Perturb.PREV
                      if self.perturb
                      else Perturb.NONE,
                  )
                  dy = (dt * (moulton_coeffs[0]) * f).type_as(
                      y0
                  ) + delta  # moulton is float64 so cast back
                  converged = self._has_converged(dy_old, dy)
                  if converged:
                      break
              if not converged:
                  warnings.warn(
                      "Functional iteration did not converge. Solution may be incorrect."
                  )
                  self.prev_f.pop()
              self._update_history(t0, f)
          return dy, f0
  
file_path: torchdiffeq/torchdiffeq/_impl/fixed_adams.py
imports: import collections
  import sys
  import torch
  import warnings
  from .solvers import FixedGridODESolver
  from .misc import _compute_error_ratio, _linf_norm
  from .misc import Perturb
  from .rk_common import rk4_alt_step_func
label_desc: Use image node_id 4 for calling the AdamsBashforthMoulton obj's underlying member method code with example usage: obj._step_func(func, t0, dt, t1, y0) and returns: rk4_alt_step_func,  f0, dy,  f0